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Course Outline
Introduction
- Overview of pattern recognition and machine learning
- Key applications in various fields
- Importance of pattern recognition in modern technology
Probability Theory, Model Selection, Decision and Information Theory
- Basics of probability theory in pattern recognition
- Concepts of model selection and evaluation
- Decision theory and its applications
- Information theory fundamentals
Probability Distributions
- Overview of common probability distributions
- Role of distributions in modeling data
- Applications in pattern recognition
Linear Models for Regression and Classification
- Introduction to linear regression
- Understanding linear classification
- Applications and limitations of linear models
Neural Networks
- Basics of neural networks and deep learning
- Training neural networks for pattern recognition
- Practical examples and case studies
Kernel Methods
- Introduction to kernel methods in pattern recognition
- Support vector machines and other kernel-based models
- Applications in high-dimensional data
Sparse Kernel Machines
- Understanding sparse models in pattern recognition
- Techniques for model sparsity and regularization
- Practical applications in data analysis
Graphical Models
- Overview of graphical models in machine learning
- Bayesian networks and Markov random fields
- Inference and learning in graphical models
Mixture Models and EM
- Introduction to mixture models
- Expectation-Maximization (EM) algorithm
- Applications in clustering and density estimation
Approximate Inference
- Techniques for approximate inference in complex models
- Variational methods and Monte Carlo sampling
- Applications in large-scale data analysis
Sampling Methods
- Importance of sampling in probabilistic models
- Markov Chain Monte Carlo (MCMC) techniques
- Applications in pattern recognition
Continuous Latent Variables
- Understanding continuous latent variable models
- Applications in dimensionality reduction and data representation
- Practical examples and case studies
Sequential Data
- Introduction to modeling sequential data
- Hidden Markov models and related techniques
- Applications in time series analysis and speech recognition
Combining Models
- Techniques for combining multiple models
- Ensemble methods and boosting
- Applications in improving model accuracy
Summary and Next Steps
Requirements
- Understanding of statistics
- Familiarity with multivariate calculus and basic linear algebra
- Some experience with probabilities
Audience
- Data analysts
- PhD students, researchers and practitioners
21 Hours
Testimonials (5)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
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The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
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Very flexible.
Frank Ueltzhoffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
